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Parametric Graph-Based Separable Transforms For Video Coding

Egilmez, Hilmi E. and Teke, Oguzhan and Said, Amir and Seregin, Vadim and Karczewicz, Marta (2020) Parametric Graph-Based Separable Transforms For Video Coding. In: 2020 IEEE International Conference on Image Processing (ICIP). IEEE , Piscataway, NJ, pp. 1306-1310. ISBN 9781728163956.

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In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction. This paper proposes a parametric approach to build graph-based separable transforms (GBSTs) for video coding. Specifically, a GBST is derived from a pair of line graphs, whose weights are determined based on two non-negative parameters. As certain choices of those parameters correspond to the discrete sine and cosine transform types used in recent video coding standards (including DCT-2, DST-7 and DCT-8), this paper further optimizes these graph parameters to better capture residual block statistics and improve video coding efficiency. The proposed GBSTs are tested on the Versatile Video Coding (VVC) reference software, and the experimental results show that about 0.4% average coding gain is achieved over the existing set of separable transforms constructed based on DCT-2, DST-7 and DCT-8 in VVC.

Item Type:Book Section
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Teke, Oguzhan0000-0002-1131-5206
Additional Information:© 2020 IEEE. This work was supported by Qualcomm Technologies, Inc. O. Teke was with Qualcomm Technologies, Inc., while most of this work has been done.
Funding AgencyGrant Number
Subject Keywords:Transform coding, learning algorithms, graph-based transforms, video coding, video compression
Record Number:CaltechAUTHORS:20201002-151458663
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Official Citation:H. E. Egilmez, O. Teke, A. Said, V. Seregin and M. Karczewicz, "Parametric Graph-Based Separable Transforms For Video Coding," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 1306-1310, doi: 10.1109/ICIP40778.2020.9191299
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105768
Deposited By: George Porter
Deposited On:05 Oct 2020 14:24
Last Modified:16 Nov 2021 18:46

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